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About

Borna Ghannadi is a robotics and control systems researcher whose work sits at the intersection of rehabilitation engineering, human-robot interaction, and advanced control theory. His research has made meaningful contributions to the development of intelligent upper extremity rehabilitation robots, with a particular focus on improving outcomes for post-stroke patients by reducing reliance on clinical therapists while delivering effective, repetitive motor training. Ghannadi's most impactful work, cited 25 times, introduced a nonlinear model predictive control framework incorporating a two-dimensional human-robot interaction model — a significant step toward adaptive, patient-responsive rehabilitation systems. Complementing this, his configuration-dependent optimal impedance control approach and earlier work on optimal impedance control reflect a sustained effort to ground rehabilitation robotics in motor learning theory and optimal control frameworks. His 2018 survey of upper extremity rehabilitation robots, alongside his contributions to parameter identification in dynamic systems featuring backlash discontinuities, further demonstrate the breadth of his technical expertise. Collectively accumulating over 75 citations, Ghannadi's research advances both the theoretical foundations and practical design of rehabilitation technology, offering promising pathways for affordable, autonomous stroke recovery systems that could meaningfully improve patient quality of life.